3D edge detection using recursive filtering: Application to scanner images

Monga, Olivier; Deriche, Rachid; Rocchisani, Jean-Marie

doi:10.1016/1049-9660(91)90006-b

Cited by 81 publications

(20 citation statements)

References 6 publications

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“…These normals are computed in the process of computing the surface curvature, as described in the previous paragraph. In addition to the surface normals, gradient images in each direction (x, y, and z) are created from the resampled isotropic grayscale images using a 3D operator proposed by Monga et al [24,25]. At each triangle, ten gradient samples are taken along the surface normal vector through the center of the triangle.…”

Section: Image Featuresmentioning

confidence: 99%

Automated pulmonary nodule CT image characterization in lung cancer screening

2015

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Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.

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Section: Image Featuresmentioning

confidence: 99%

Automated pulmonary nodule CT image characterization in lung cancer screening

2015

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“…It has the qualities of good edge detection, accurate localization, and one response to an edge. Deriche [10] developed an operator that complies with these criteria even better. In this case, the separable and recursive low-pass filter is the integral of the corresponding edge detector.…”

Section: B Optimal Low-pass Filtermentioning

confidence: 96%

“…In this case, the separable and recursive low-pass filter is the integral of the corresponding edge detector. The formula of the resulting 1-D operator is (13) The implementation of this filter has been described in the literature [9], [10], [12]. In contrast to adjusting the size of a kernel operator, which involves many computation steps, the smoothing effect of can be controlled by simply adjusting the parameter .…”

Section: B Optimal Low-pass Filtermentioning

confidence: 99%

“…This is important for processing and storing large 3-D image data. We introduce two low-pass filters, one based on a separable Butterworth low-pass filter design [4] and the other one based on the principles of the Monga-Deriche's recursive operator [9], [10] that satisfies the design criteria of Canny [11] and the optimal filters [12].…”

Section: Recursive Low-pass Filter Implementationmentioning

confidence: 99%

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Realizations of fast 2-D/3-D image filtering and enhancement

Tang

Zhuang

2001

IEEE Trans. Med. Imaging

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This paper proposes a novel algorithm for multidimensional image enhancement based on a fuzzy domain enhancement method, and an implementation of a recursive and separable low-pass filter. Considering a smoothed image as a fuzzy data set, each pixel in an image is processed independently, using fuzzy domain transformation and enhancement of both the dynamic range and the local gray level variations. The algorithm has the advantages of being fast and adaptive, so it can be used in real-time image processing applications and for multidimensional data with low computational cost. It also has the ability to reduce noise and unwanted background that may affect the visualization quality of two-dimensional (2-D)/three-dimensional (3-D) data. Examples for the applications of the algorithm are given for mammograms, ultrasound 3-D images, and photographic images.

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“…To decrease the effects of noise in the gradient map these operations should use a large convolution kernel that considers a local region of the image. Previous work [8] has considered what makes a good kernel function and we have followed that approach and used a previously defined kernel function (K) as…”

Section: Theorymentioning

confidence: 99%

Reproducibility and accuracy of automated measurement for dynamic arterial lumen area by cardiovascular magnetic resonance

Jackson

Shirodaria

Lee

et al. 2009

Int J Cardiovasc Imaging

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Bright blood cine images acquired using magnetic resonance imaging contain simple contrast that is tractable to automated analysis, which can be used to derive a measure of arterial compliance that is known to correlate with disease severity. The purpose of this work was to evaluate whether automated methods could be used reliably on a clinically relevant population, and to assess the precision of these measurements so that it could be compared with expert manual assessment. In this paper we apply an algorithm similar to that used by Krug et al., and the exact processing steps are described in detail to allowing easy reproduction of our methods. Phantoms of different sizes have been assessed and the MRI measurements are found to correlate well (r = 0.9998) with physical measurement. Reproducibility assessment was performed on 33 CAD subjects in three anatomical locations along the aorta. Six normal volunteers and ten patients with more severe aortic plaques were investigated to assess reproducibility and sensitivity to pathological changes, respectively. The performance was also assessed on carotid vessels in 40 patients with known arterial plaques. In the human aorta the method is found to be robust (failing in only 7% of cases, all due to clear errors with image acquisition), and to be quantifiably consistent with expert clinical measurement, but showing smaller errors than that approach [<1.21% (5.62 mm(2)) manual vs. <0.58% (2.71 mm(2)) automated, for the aortic area] and with reduced bias, and operated correctly in advanced disease. We have proved over a large number of subjects the superiority of this automated method for evaluating dynamic area changes over the Gold-standard manual approach.

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3D edge detection using recursive filtering: Application to scanner images

Cited by 81 publications

References 6 publications

Automated pulmonary nodule CT image characterization in lung cancer screening

Automated pulmonary nodule CT image characterization in lung cancer screening

Realizations of fast 2-D/3-D image filtering and enhancement

Reproducibility and accuracy of automated measurement for dynamic arterial lumen area by cardiovascular magnetic resonance

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